Devices that capture and produce still images, video images, audio recordings, animations, and other types of audio, visual, and written content allow the creation of large collections of media assets, including digital media assets. These assets can be stored in a common storage location or distributed across a wide variety of storage locations. The assets may also be physically stored on a wide variety of devices such as tape or computer disk. As the number and size of media assets increases and the storage devices become large and varied, it is increasingly difficult to navigate through the assets to locate and access particular content of interest.
Media companies have thousands of assets with complex, opaque, and multi-dimensional relationships to each other. The assets can be spread across many different institutions, facilities, and vendors. Broadcasters and those entities that deliver these assets must navigate these interconnections on a daily basis, and problems in one system can quickly transmit throughout the enterprise, impacting any number of other business processes in their wake. Understanding and managing relationships between assets is important for many key functions in the enterprise, including content delivery, licensing, advertising, and financial reconciliation. For example, advertising analysts need a clear, detailed understanding of asset viewing and license limitations to calculate values and expected returns on these assets. Yet, broadcasters struggle to understand and analyze the complex web of relationships that are fundamental to their daily operations.
Media asset management involves many diverse disciplines and requires data of various kinds, from a wide array of sources. Multi-platform media companies harness and manage assets from disparate sources to deliver interactive and engaging user experiences. The existing processes for gathering asset metadata and capturing relationships between assets is often manual, ad-hoc, and frequently difficult to repeat or update. Conventional commercial Media Asset Management (MAM) systems require inclusion of all metadata into a single MAM system to capture relationships. This limits the choice of system to a single vendor or suite. The result is a monolithic system that cannot change quickly as new asset types or business needs are introduced.
Many current asset management tools and techniques focusing on relational databases lack both the necessary speed and flexibility to analyze and traverse networks of relationships in a media asset environment.
Distribution of video content is rapidly expanding across multiple platforms, each with different display characteristics and ability to interact with related content. In order to program and distribute efficiently across multiple platforms, content distributors must be able to quickly select a collection of related assets and bundle them for distribution.